New diagnostic set of nine proteins distinguishes women with ovarian cancer from healthy women with greater sensitivity and specificity than reported before.

They compared samples from women with ovarian cancer having surgery in the course of treatment and from volunteers who had gynecological surgery for reasons unrelated to cancer, such as uterine fibroids or excessive bleeding.
Bodily fluids contain many proteins. Strong signals from the most common proteins can mask signals from smaller amounts of cancer-linked proteins that might also be present. To overcome that difficulty, researchers isolated microvesicles from the uterine fluid. Because microvesicles are shed from cells, they contain almost none of the signal-masking plasma proteins.
Using proteomics, the researchers compared thousands of proteins in uterine microvesicles from 12 healthy volunteers and 12 cancer patients. Then they used machine learning algorithms to search for patterns that could distinguish between the samples.
"We developed a diagnostic set of nine proteins that distinguishes women with ovarian cancer from healthy women with greater sensitivity and specificity than reported before," Levanon said.
The researchers then tested the set's accuracy in a cohort of 152 women, 37 of whom were known to have ovarian cancer. The test had 70 percent diagnostic sensitivity, meaning that it correctly detected cancer 25 of the 37 study participants who truly had cancer; and 76 percent specificity, meaning that it correctly identified about three out of every four healthy volunteers as healthy. It outperformed previous proteomics-based tests, which had less than 60 percent sensitivity.
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Source-Eurekalert